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Soybean and Turmeric Nanoemulsion for the Prevention and Management of Striae Gravidarum: A Narrative Literature Review Dianita Wijayanti; Melyana Nurul Widyawati; Fatimah Fatimah
Jurnal Kesehatan Ibu dan Anak Vol. 19 No. 1
Publisher : Poltekkes Kemenkes Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29238/kia.v19i1.3043

Abstract

Striae gravidarum is a common dermatological condition affecting pregnant and postpartum women. Conventional topical therapies often demonstrate limited effectiveness because of poor dermal penetration and the physicochemical instability of natural bioactive compounds. Nanoformulation technologies may enhance the delivery and therapeutic performance of plant-derived agents such as turmeric (Curcuma longa) and soybean (Glycine max). This review aimed to evaluate current evidence regarding the potential of turmeric and soybean nanoformulations for skin regeneration and the management of striae gravidarum. A literature review was conducted between June and September 2025 using PubMed, ScienceDirect, Wiley Online Library, and Google Scholar. Articles published between 2020 and 2025 in English or Indonesian were screened using predefined eligibility criteria. Seventeen eligible studies were included in the narrative synthesis. Eleven studies (64.7%) demonstrated that curcumin-based nanoformulations enhanced antioxidant activity, reduced inflammation, and stimulated collagen synthesis. Seven studies reported improved dermal penetration with particle sizes below 250 nm and encapsulation or absorption efficiencies exceeding 80%. Soybean-derived isoflavones increased type I collagen synthesis by up to 43.9% and reduced reactive oxygen species (ROS)-mediated oxidative stress. Clinical studies reported significant improvements in skin condition, hydration, elasticity, and striae severity following botanical interventions. However, no clinical trial has directly evaluated a combined turmeric–soybean nanoemulsion formulation for striae gravidarum. Current evidence suggests that turmeric and soybean nanoformulations possess promising regenerative, antioxidant, and collagen-stimulating properties relevant to striae gravidarum management. Nevertheless, evidence remains predominantly preclinical, and well-designed clinical trials are required to establish the efficacy, safety, and therapeutic value of combined turmeric–soybean nanoemulsion formulations in pregnant and postpartum populations
Application of 3D Printing Based on the Brain MRI Dicom Dataset as a Learning Media for MRI Anatomy for Radiology Students Alifia Dian Rizqi Arshyva; Fatimah Fatimah; Darmini Darmini
International Journal of Health and Social Behavior Vol. 3 No. 2 (2026): May: International Journal of Health and Social Behavior
Publisher : Asosiasi Riset Ilmu Kesehatan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijhsb.v3i2.626

Abstract

Background The brain is a complex organ and quite difficult to study, so effective learning methods are needed to improve understanding of brain anatomy. 3D printing technology has been widely used in the medical world.medicine, especially as a learning medium for anatomy for medical students, but currently there are no radiology educational institutions that provide 3D learning aids printing which are converted directly from DICOM MRI image results.Research Purposes : To prove the difference in application between 3D printing and 2D DICOM MRI brain datasets in learning brain anatomy in radiology students. Method, The research was conducted using a design true experiment with a sample of 76 students divided into experimental and control groups. The experimental group used 3D printing media, while the control group used 2D DICOM images.Results, The average score of the experimental group was higher (88.76) than the control group (56.21). Statistical tests showed a significant difference between the two groups (p < 0.05). Conclusion, Learning brain anatomy using 3D printing media is more effective than the 2D DICOM image learning method (N-Gain 0.776).